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Robust Reinforcement Learning: A Review of Foundations and Recent Advances

Moos, Janosch ; Hansel, Kay ; Abdulsamad, Hany ; Stark, Svenja ; Clever, Debora ; Peters, Jan (2022)
Robust Reinforcement Learning: A Review of Foundations and Recent Advances.
In: Machine Learning and Knowledge Extraction, 2022, 4 (1)
doi: 10.26083/tuprints-00021118
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Item Type: Article
Type of entry: Secondary publication
Title: Robust Reinforcement Learning: A Review of Foundations and Recent Advances
Language: English
Date: 11 April 2022
Place of Publication: Darmstadt
Year of primary publication: 2022
Publisher: MDPI
Journal or Publication Title: Machine Learning and Knowledge Extraction
Volume of the journal: 4
Issue Number: 1
DOI: 10.26083/tuprints-00021118
Corresponding Links:
Origin: Secondary publication DeepGreen

Reinforcement learning (RL) has become a highly successful framework for learning in Markov decision processes (MDP). Due to the adoption of RL in realistic and complex environments, solution robustness becomes an increasingly important aspect of RL deployment. Nevertheless, current RL algorithms struggle with robustness to uncertainty, disturbances, or structural changes in the environment. We survey the literature on robust approaches to reinforcement learning and categorize these methods in four different ways: (i) Transition robust designs account for uncertainties in the system dynamics by manipulating the transition probabilities between states; (ii) Disturbance robust designs leverage external forces to model uncertainty in the system behavior; (iii) Action robust designs redirect transitions of the system by corrupting an agent’s output; (iv) Observation robust designs exploit or distort the perceived system state of the policy. Each of these robust designs alters a different aspect of the MDP. Additionally, we address the connection of robustness to the risk-based and entropy-regularized RL formulations. The resulting survey covers all fundamental concepts underlying the approaches to robust reinforcement learning and their recent advances.

Uncontrolled Keywords: reinforcement learning, robustness, min-max optimization
Status: Publisher's Version
URN: urn:nbn:de:tuda-tuprints-211188
Classification DDC: 000 Generalities, computers, information > 004 Computer science
600 Technology, medicine, applied sciences > 620 Engineering and machine engineering
Divisions: 16 Department of Mechanical Engineering > Institute for Mechatronic Systems in Mechanical Engineering (IMS)
20 Department of Computer Science > Intelligent Autonomous Systems
Date Deposited: 11 Apr 2022 11:34
Last Modified: 14 Nov 2023 19:04
SWORD Depositor: Deep Green
URI: https://tuprints.ulb.tu-darmstadt.de/id/eprint/21118
PPN: 500750009
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